[seaborn] seaborn學習筆記11——繪圖實例(3) Drawing example(3)

11 繪圖實例(3) Drawing example(3)(代碼下載)

本文主要講述seaborn官網相關函數繪圖實例。具體內容有：

1. Plotting a diagonal correlation matrix(heatmap)
2. Scatterplot with marginal ticks(JointGrid)
3. Multiple bivariate KDE plots(kdeplot)
4. Multiple linear regression(lmplot)
5. Paired density and scatterplot matrix(PairGrid)
6. Paired categorical plots(PairGrid)
7. Dot plot with several variables(PairGrid)
8. Plotting a three-way ANOVA(catplot)
9. Linear regression with marginal distributions(jointplot)
10. Plotting model residuals(residplot)

# import packages
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt

1. Plotting a diagonal correlation matrix(heatmap)

# 讀取字母表
from string import ascii_letters

# Generate a large random dataset 生成數據集
rs = np.random.RandomState(33)
d = pd.DataFrame(data=rs.normal(size=(100, 26)),
                 columns=list(ascii_letters[26:]))

# Compute the correlation matrix 計算相關係數
corr = d.corr()

# Generate a mask for the upper triangle 生成掩模
mask = np.zeros_like(corr, dtype=np.bool)
mask[np.triu_indices_from(mask)] = True

d# Set up the matplotlib figure 設置圖大小
f, ax = plt.subplots(figsize=(11, 9))

# Generate a custom diverging colormap 設置顏色
cmap = sns.diverging_palette(220, 10, as_cmap=True)

# Draw the heatmap with the mask and correct aspect ratio
# square表都是正方形
sns.heatmap(corr, mask=mask, cmap=cmap, vmax=.3, center=0,
            square=True, linewidths=.5, cbar_kws={"shrink": .5});

2. Scatterplot with marginal ticks(JointGrid)

sns.set(style="white", color_codes=True)

# Generate a random bivariate dataset
rs = np.random.RandomState(9)
mean = [0, 0]
cov = [(1, 0), (0, 2)]
x, y = rs.multivariate_normal(mean, cov, 100).T

# Use JointGrid directly to draw a custom plot
# 創建一個繪圖表格區域，設置好x,y對應數據
grid = sns.JointGrid(x, y, space=0, height=6, ratio=50)
# 在聯合分佈上畫出散點圖
grid.plot_joint(plt.scatter, color="g")
# 在邊緣分佈上再作圖
grid.plot_marginals(sns.rugplot, height=1, color="g");

3. Multiple bivariate KDE plots(kdeplot)

sns.set(style="darkgrid")
iris = sns.load_dataset("iris")

# Subset the iris dataset by species
# 單獨篩選對應類的數據
setosa = iris.query("species == 'setosa'")
virginica = iris.query("species == 'virginica'")

# Set up the figure
f, ax = plt.subplots(figsize=(8, 8))
# 設置軸的縮放比例，equal表示x,y軸同等縮放比例
ax.set_aspect("equal")

# Draw the two density plots
# 畫核密度圖
# shade表示添加陰影，shade_lowest表示兩個核密度圖相疊時，核密度小的部分不畫出來
ax = sns.kdeplot(setosa.sepal_width, setosa.sepal_length,
                 cmap="Reds", shade=True, shade_lowest=False)
ax = sns.kdeplot(virginica.sepal_width, virginica.sepal_length,
                 cmap="Blues", shade=True, shade_lowest=False)

# Add labels to the plot
# 添加顏色
red = sns.color_palette("Reds")[-2]
blue = sns.color_palette("Blues")[-2]
ax.text(2.5, 8.2, "virginica", size=16, color=blue)
ax.text(3.8, 4.5, "setosa", size=16, color=red);

C:\ProgramData\Anaconda3\lib\site-packages\scipy\stats\stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.
  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval

4. Multiple linear regression(lmplot)

# Load the iris dataset 讀取數據
iris = sns.load_dataset("iris")

# Plot sepal with as a function of sepal_length across days
# 畫散點圖，lmplot默認參數，以hue設定不同種類
# truncate爲true表示現植迴歸擬合曲線繪圖時只畫出有數據的部分
g = sns.lmplot(x="sepal_length", y="sepal_width", hue="species",
               truncate=True, height=5, data=iris);

# Use more informative axis labels than are provided by default
# 設置橫豎座標軸label
g.set_axis_labels("Sepal length (mm)", "Sepal width (mm)");

5. Paired density and scatterplot matrix(PairGrid)

sns.set(style="white")

df = sns.load_dataset("iris")
# 製作散點圖矩陣
# diag_sharey是否共享y軸
g = sns.PairGrid(df, diag_sharey=False)
# 下三角繪多變量核密度圖
g.map_lower(sns.kdeplot)
# 上三角繪散點圖
g.map_upper(sns.scatterplot)
# 對角線繪單變量核密度圖，lw表示線條粗細
g.map_diag(sns.kdeplot, lw=3);

6. Paired categorical plots(PairGrid)

sns.set(style="whitegrid")

# Load the example Titanic dataset
titanic = sns.load_dataset("titanic")

# Set up a grid to plot survival probability against several variables
# 製作散點圖矩陣
# y軸爲survived值，x_vars設定x軸
g = sns.PairGrid(titanic, y_vars="survived",
                 x_vars=["class", "sex", "who", "alone"],
                 height=5, aspect=.5)
# Draw a seaborn pointplot onto each Axes
# 製作折線圖, errwidth表示上下標準注的長度，其中各點代表平均值
g.map(sns.pointplot, scale=1.3, errwidth=4, color="xkcd:plum")
g.set(ylim=(0, 1))
sns.despine(fig=g.fig, left=True);

7. Dot plot with several variables(PairGrid)

sns.set(style="whitegrid")

# Load the dataset
crashes = sns.load_dataset("car_crashes")

# Make the PairGrid
# 按crash排序的值繪圖，x_vars，y_vars表示x軸或者y軸
g = sns.PairGrid(crashes.sort_values("total", ascending=False),
                 x_vars=crashes.columns[:-3], y_vars=["abbrev"],
                 height=10, aspect=.25)
# Draw a dot plot using the stripplot function
g.map(sns.stripplot, size=10, orient="h",
      palette="ch:s=1,r=-.1,h=1_r", linewidth=1, edgecolor="w")
# Use the same x axis limits on all columns and add better labels
# 設置x軸，x標籤
g.set(xlim=(0, 25), xlabel="Crashes", ylabel="")

# Use semantically meaningful titles for the columns
titles = ["Total crashes", "Speeding crashes", "Alcohol crashes",
          "Not distracted crashes", "No previous crashes"]

#去除軸線
for ax, title in zip(g.axes.flat, titles):

    # Set a different title for each axes
    ax.set(title=title)

    # Make the grid horizontal instead of vertical
    ax.xaxis.grid(False)
    ax.yaxis.grid(True)

sns.despine(left=True, bottom=True);

8. Plotting a three-way ANOVA(catplot)

# Load the example exercise dataset
df = sns.load_dataset("exercise")

# Draw a pointplot to show pulse as a function of three categorical factors
# 分類型數據作座標軸畫圖catplot,
# col表示用什麼變量對圖像在橫座標方向分列
# hue表示在單個維度上用某個變量區分;
# capsize表示延伸線的長度
g = sns.catplot(x="time", y="pulse", hue="kind", col="diet",
                capsize=0.2, palette="YlGnBu_d", height=6, aspect=.75,
                kind="point", data=df)
g.despine(left=True);

9. Linear regression with marginal distributions(jointplot)

sns.set(style="darkgrid")

tips = sns.load_dataset("tips")
# 設置聯合圖像，類型是"reg"迴歸圖
g = sns.jointplot("total_bill", "tip", data=tips, kind="reg",
                  xlim=(0, 60), ylim=(0, 12), color="m", height=7)

10. Plotting model residuals(residplot)

sns.set(style="whitegrid")

# Make an example dataset with y ~ x
rs = np.random.RandomState(7)
x = rs.normal(2, 1, 75)
y = 2 + 1.5 * x + rs.normal(0, 2, 75)

# Plot the residuals after fitting a linear model 殘差圖
# 中間曲線爲殘差曲線((對比一階擬合直線的殘差)),lowess曲線平滑
sns.residplot(x, y, lowess=True, color="g");